Amazon SageMaker Unified Studio now supports multiple code spaces within projects for IAM domains
Amazon Web Services has expanded Amazon SageMaker Unified Studio to support multiple code spaces within individual projects for IAM domains, removing the previous limitation of one JupyterLab space and one Code Editor space per project. The enhancement allows data scientists and developers to run parallel workstreams or experiments with different compute and storage configurations within the same collaborative project, addressing scalability challenges as workloads grow more complex. Each code space maintains its own persistent Amazon EBS volume, ensuring independent preservation of files, data, and session states across different development environments. Users can dynamically scale compute and storage resources per space, pause and resume environments as needed, and customize runtime configurations for specific tasks. The spaces can be accessed through dedicated browser tabs or connected to local IDEs, with full Amazon Q paid tier support included. The feature is now available across all AWS regions where SageMaker Unified Studio operates, targeting data science teams that require isolated development environments for concurrent projects while maintaining unified project collaboration and governance.
Why It Matters
This update addresses a significant workflow limitation for enterprise data science teams who often need to run multiple experiments or data processing tasks simultaneously with different resource requirements. By enabling parallel development environments within unified projects, AWS is positioning SageMaker to better compete with standalone ML development platforms while maintaining the governance and collaboration benefits of centralized project management. The feature particularly strengthens AWS's appeal to larger organizations where data science teams need both flexibility in resource allocation and consistency in project oversight.
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